To deal with these issues, efficient GCN training has been heavily investigated, sparking the development of node partitioning methodologies.
These schemes can be roughly categorized into neighborhood sampling [1]}, [2]}, [3]} and graph partitioning [4]}, [5]} approaches.
The goal is to partition a large graph into multiple smaller graphs that can be used to train the GCN, thus allowing for the formation of mini-batches.
In this way, GCNs gain the ability to handle larger graphs during training, thus expanding their potential into the realm of big data.
